Update dependency numpy to v2 (!116) · Merge requests · Water Mission Area / nhgf / Tools / gdptools

igswsihw-wmadepbot requested to merge renovate/numpy-2.x into develop Nov 24, 2024

This MR contains the following updates:

Package	Type	Update	Change
numpy (changelog)	dependencies	major	`<2.0` -> `<2.3`

Release Notes

numpy/numpy (numpy)

`v2.2.0`: 2.2.0 (Dec 8, 2024)

Compare Source

NumPy 2.2.0 Release Notes

The NumPy 2.2.0 release is quick release that brings us back into sync with the usual twice yearly release cycle. There have been an number of small cleanups, as well as work bringing the new StringDType to completion and improving support for free threaded Python. Highlights are:

New functions matvec and vecmat, see below.
Many improved annotations.
Improved support for the new StringDType.
Improved support for free threaded Python
Fixes for f2py

This release supports Python versions 3.10-3.13.

Deprecations

_add_newdoc_ufunc is now deprecated. ufunc.__doc__ = newdoc should be used instead.

(gh-27735)

Expired deprecations

bool(np.array([])) and other empty arrays will now raise an error. Use arr.size > 0 instead to check whether an array has no elements.

(gh-27160)

Compatibility notes

numpy.cov now properly transposes single-row (2d array) design matrices when rowvar=False. Previously, single-row design matrices would return a scalar in this scenario, which is not correct, so this is a behavior change and an array of the appropriate shape will now be returned.

(gh-27661)

New Features

New functions for matrix-vector and vector-matrix products

Two new generalized ufuncs were defined:
- numpy.matvec - matrix-vector product, treating the arguments as stacks of matrices and column vectors, respectively.
- numpy.vecmat - vector-matrix product, treating the arguments as stacks of column vectors and matrices, respectively. For complex vectors, the conjugate is taken.
These add to the existing numpy.matmul as well as to numpy.vecdot, which was added in numpy 2.0.

Note that numpy.matmul never takes a complex conjugate, also not when its left input is a vector, while both numpy.vecdot and numpy.vecmat do take the conjugate for complex vectors on the left-hand side (which are taken to be the ones that are transposed, following the physics convention).

(gh-25675)
np.complexfloating[T, T] can now also be written as np.complexfloating[T]

(gh-27420)
UFuncs now support __dict__ attribute and allow overriding __doc__ (either directly or via ufunc.__dict__["__doc__"]). __dict__ can be used to also override other properties, such as __module__ or __qualname__.

(gh-27735)
The "nbit" type parameter of np.number and its subtypes now defaults to typing.Any. This way, type-checkers will infer annotations such as x: np.floating as x: np.floating[Any], even in strict mode.

(gh-27736)

Improvements

The datetime64 and timedelta64 hashes now correctly match the Pythons builtin datetime and timedelta ones. The hashes now evaluated equal even for equal values with different time units.

(gh-14622)
Fixed a number of issues around promotion for string ufuncs with StringDType arguments. Mixing StringDType and the fixed-width DTypes using the string ufuncs should now generate much more uniform results.

(gh-27636)
Improved support for empty memmap. Previously an empty memmap would fail unless a non-zero offset was set. Now a zero-size memmap is supported even if offset=0. To achieve this, if a memmap is mapped to an empty file that file is padded with a single byte.

(gh-27723)
A regression has been fixed which allows F2PY users to expose variables to Python in modules with only assignments, and also fixes situations where multiple modules are present within a single source file.

(gh-27695)

Performance improvements and changes

Improved multithreaded scaling on the free-threaded build when many threads simultaneously call the same ufunc operations.

(gh-27896)
NumPy now uses fast-on-failure attribute lookups for protocols. This can greatly reduce overheads of function calls or array creation especially with custom Python objects. The largest improvements will be seen on Python 3.12 or newer.

(gh-27119)
OpenBLAS on x86_64 and i686 is built with fewer kernels. Based on benchmarking, there are 5 clusters of performance around these kernels: PRESCOTT NEHALEM SANDYBRIDGE HASWELL SKYLAKEX.
OpenBLAS on windows is linked without quadmath, simplifying licensing
Due to a regression in OpenBLAS on windows, the performance improvements when using multiple threads for OpenBLAS 0.3.26 were reverted.

(gh-27147)
NumPy now indicates hugepages also for large np.zeros allocations on linux. Thus should generally improve performance.

(gh-27808)

Changes

numpy.fix now won't perform casting to a floating data-type for integer and boolean data-type input arrays.

(gh-26766)
The type annotations of numpy.float64 and numpy.complex128 now reflect that they are also subtypes of the built-in float and complex types, respectively. This update prevents static type-checkers from reporting errors in cases such as:
```
x: float = numpy.float64(6.28)  # valid
z: complex = numpy.complex128(-1j)  # valid
```
(gh-27334)
The repr of arrays large enough to be summarized (i.e., where elements are replaced with ...) now includes the shape of the array, similar to what already was the case for arrays with zero size and non-obvious shape. With this change, the shape is always given when it cannot be inferred from the values. Note that while written as shape=..., this argument cannot actually be passed in to the np.array constructor. If you encounter problems, e.g., due to failing doctests, you can use the print option legacy=2.1 to get the old behaviour.

(gh-27482)
Calling __array_wrap__ directly on NumPy arrays or scalars now does the right thing when return_scalar is passed (Added in NumPy 2). It is further safe now to call the scalar __array_wrap__ on a non-scalar result.

(gh-27807)
Bump the musllinux CI image and wheels to 1_2 from 1_1. This is because 1_1 is end of life.

(gh-27088)
The NEP 50 promotion state settings are now removed. They were always meant as temporary means for testing. A warning will be given if the environment variable is set to anything but NPY_PROMOTION_STATE=weak while _set_promotion_state and _get_promotion_state are removed. In case code used _no_nep50_warning, a contextlib.nullcontext could be used to replace it when not available.

(gh-27156)

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140dd80ff8981a583a60980be1a655068f8adebf7a45a06a6858c873fcdcd4a0  numpy-2.2.0.tar.gz

`v2.1.3`: 2.1.3 (Nov 2, 2024)

Compare Source

NumPy 2.1.3 Release Notes

NumPy 2.1.3 is a maintenance release that fixes bugs and regressions discovered after the 2.1.2 release. This release also adds support for free threaded Python 3.13 on Windows.

The Python versions supported by this release are 3.10-3.13.

Improvements

Fixed a number of issues around promotion for string ufuncs with StringDType arguments. Mixing StringDType and the fixed-width DTypes using the string ufuncs should now generate much more uniform results.

(gh-27636)

Changes

numpy.fix now won't perform casting to a floating data-type for integer and boolean data-type input arrays.

(gh-26766)

Contributors

A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Abhishek Kumar +
Austin +
Benjamin A. Beasley +
Charles Harris
Christian Lorentzen
Marcel Telka +
Matti Picus
Michael Davidsaver +
Nathan Goldbaum
Peter Hawkins
Raghuveer Devulapalli
Ralf Gommers
Sebastian Berg
dependabot[bot]
kp2pml30 +

Pull requests merged

A total of 21 pull requests were merged for this release.

#27512: MAINT: prepare 2.1.x for further development
#27537: MAINT: Bump actions/cache from 4.0.2 to 4.1.1
#27538: MAINT: Bump pypa/cibuildwheel from 2.21.2 to 2.21.3
#27539: MAINT: MSVC does not support #warning directive
#27543: BUG: Fix user dtype can-cast with python scalar during promotion
#27561: DEV: bump python to 3.12 in environment.yml
#27562: BLD: update vendored Meson to 1.5.2
#27563: BUG: weighted quantile for some zero weights (#27549)
#27565: MAINT: Use miniforge for macos conda test.
#27566: BUILD: satisfy gcc-13 pendantic errors
#27569: BUG: handle possible error for PyTraceMallocTrack
#27570: BLD: start building Windows free-threaded wheels [wheel build]
#27571: BUILD: vendor tempita from Cython
#27574: BUG: Fix warning "differs in levels of indirection" in npy_atomic.h...
#27592: MAINT: Update Highway to latest
#27593: BUG: Adjust numpy.i for SWIG 4.3 compatibility
#27616: BUG: Fix Linux QEMU CI workflow
#27668: BLD: Do not set __STDC_VERSION__ to zero during build
#27669: ENH: fix wasm32 runtime type error in numpy._core
#27672: BUG: Fix a reference count leak in npy_find_descr_for_scalar.
#27673: BUG: fixes for StringDType/unicode promoters

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`v2.1.2`: 2.1.2 (Oct 5, 2024)

Compare Source

NumPy 2.1.2 Release Notes

NumPy 2.1.2 is a maintenance release that fixes bugs and regressions discovered after the 2.1.1 release.

The Python versions supported by this release are 3.10-3.13.

Contributors

A total of 11 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Chris Sidebottom
Ishan Koradia +
João Eiras +
Katie Rust +
Marten van Kerkwijk
Matti Picus
Nathan Goldbaum
Peter Hawkins
Pieter Eendebak
Slava Gorloff +

Pull requests merged

A total of 14 pull requests were merged for this release.

#27333: MAINT: prepare 2.1.x for further development
#27400: BUG: apply critical sections around populating the dispatch cache
#27406: BUG: Stub out get_build_msvc_version if distutils.msvccompiler...
#27416: BUILD: fix missing include for std::ptrdiff_t for C++23 language...
#27433: BLD: pin setuptools to avoid breaking numpy.distutils
#27437: BUG: Allow unsigned shift argument for np.roll
#27439: BUG: Disable SVE VQSort
#27471: BUG: rfftn axis bug
#27479: BUG: Fix extra decref of PyArray_UInt8DType.
#27480: CI: use PyPI not scientific-python-nightly-wheels for CI doc...
#27481: MAINT: Check for SVE support on demand
#27484: BUG: initialize the promotion state to be weak
#27501: MAINT: Bump pypa/cibuildwheel from 2.20.0 to 2.21.2
#27506: BUG: avoid segfault on bad arguments in ndarray.__array_function__

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`v2.1.1`: 2.1.1 (Sep 3, 2024)

Compare Source

NumPy 2.1.1 Release Notes

NumPy 2.1.1 is a maintenance release that fixes bugs and regressions discovered after the 2.1.0 release.

The Python versions supported by this release are 3.10-3.13.

Contributors

A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Andrew Nelson
Charles Harris
Mateusz Sokół
Maximilian Weigand +
Nathan Goldbaum
Pieter Eendebak
Sebastian Berg

Pull requests merged

A total of 10 pull requests were merged for this release.

#27236: REL: Prepare for the NumPy 2.1.0 release [wheel build]
#27252: MAINT: prepare 2.1.x for further development
#27259: BUG: revert unintended change in the return value of set_printoptions
#27266: BUG: fix reference counting bug in __array_interface__ implementation...
#27267: TST: Add regression test for missing descr in array-interface
#27276: BUG: Fix #27256 and #27257
#27278: BUG: Fix array_equal for numeric and non-numeric scalar types
#27287: MAINT: Update maintenance/2.1.x after the 2.0.2 release
#27303: BLD: cp311- macosx_arm64 wheels [wheel build]
#27304: BUG: f2py: better handle filtering of public/private subroutines

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`v2.1.0`: 2.1.0 (Aug 18, 2024)

Compare Source

NumPy 2.1.0 Release Notes

NumPy 2.1.0 provides support for the upcoming Python 3.13 release and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get us back into our usual release cycle after the extended development of 2.0. The highlights for this release are:

Support for the array-api 2023.12 standard.
Support for Python 3.13.
Preliminary support for free threaded Python 3.13.

Python versions 3.10-3.13 are supported in this release.

New functions

New function `numpy.unstack`

A new function np.unstack(array, axis=...) was added, which splits an array into a tuple of arrays along an axis. It serves as the inverse of [numpy.stack]{.title-ref}.

(gh-26579)

Deprecations

The fix_imports keyword argument in numpy.save is deprecated. Since NumPy 1.17, numpy.save uses a pickle protocol that no longer supports Python 2, and ignored fix_imports keyword. This keyword is kept only for backward compatibility. It is now deprecated.

(gh-26452)
Passing non-integer inputs as the first argument of [bincount]{.title-ref} is now deprecated, because such inputs are silently cast to integers with no warning about loss of precision.

(gh-27076)

Expired deprecations

Scalars and 0D arrays are disallowed for numpy.nonzero and numpy.ndarray.nonzero.

(gh-26268)
set_string_function internal function was removed and PyArray_SetStringFunction was stubbed out.

(gh-26611)

C API changes

API symbols now hidden but customizable

NumPy now defaults to hide the API symbols it adds to allow all NumPy API usage. This means that by default you cannot dynamically fetch the NumPy API from another library (this was never possible on windows).

If you are experiencing linking errors related to PyArray_API or PyArray_RUNTIME_VERSION, you can define the NPY_API_SYMBOL_ATTRIBUTE to opt-out of this change.

If you are experiencing problems due to an upstream header including NumPy, the solution is to make sure you #include "numpy/ndarrayobject.h" before their header and import NumPy yourself based on including-the-c-api.

(gh-26103)

Many shims removed from npy_3kcompat.h

Many of the old shims and helper functions were removed from npy_3kcompat.h. If you find yourself in need of these, vendor the previous version of the file into your codebase.

(gh-26842)

New `PyUFuncObject` field `process_core_dims_func`

The field process_core_dims_func was added to the structure PyUFuncObject. For generalized ufuncs, this field can be set to a function of type PyUFunc_ProcessCoreDimsFunc that will be called when the ufunc is called. It allows the ufunc author to check that core dimensions satisfy additional constraints, and to set output core dimension sizes if they have not been provided.

(gh-26908)

New Features

Preliminary Support for Free-Threaded CPython 3.13

CPython 3.13 will be available as an experimental free-threaded build. See https://py-free-threading.github.io, PEP 703 and the CPython 3.13 release notes for more detail about free-threaded Python.

NumPy 2.1 has preliminary support for the free-threaded build of CPython 3.13. This support was enabled by fixing a number of C thread-safety issues in NumPy. Before NumPy 2.1, NumPy used a large number of C global static variables to store runtime caches and other state. We have either refactored to avoid the need for global state, converted the global state to thread-local state, or added locking.

Support for free-threaded Python does not mean that NumPy is thread safe. Read-only shared access to ndarray should be safe. NumPy exposes shared mutable state and we have not added any locking to the array object itself to serialize access to shared state. Care must be taken in user code to avoid races if you would like to mutate the same array in multiple threads. It is certainly possible to crash NumPy by mutating an array simultaneously in multiple threads, for example by calling a ufunc and the resize method simultaneously. For now our guidance is: "don't do that". In the future we would like to provide stronger guarantees.

Object arrays in particular need special care, since the GIL previously provided locking for object array access and no longer does. See Issue #27199 for more information about object arrays in the free-threaded build.

If you are interested in free-threaded Python, for example because you have a multiprocessing-based workflow that you are interested in running with Python threads, we encourage testing and experimentation.

If you run into problems that you suspect are because of NumPy, please open an issue, checking first if the bug also occurs in the "regular" non-free-threaded CPython 3.13 build. Many threading bugs can also occur in code that releases the GIL; disabling the GIL only makes it easier to hit threading bugs.

(gh-26157)

`f2py` can generate freethreading-compatible C extensions

Pass --freethreading-compatible to the f2py CLI tool to produce a C extension marked as compatible with the free threading CPython interpreter. Doing so prevents the interpreter from re-enabling the GIL at runtime when it imports the C extension. Note that f2py does not analyze fortran code for thread safety, so you must verify that the wrapped fortran code is thread safe before marking the extension as compatible.

(gh-26981)

numpy.reshape and numpy.ndarray.reshape now support shape and copy arguments.

(gh-26292)
NumPy now supports DLPack v1, support for older versions will be deprecated in the future.

(gh-26501)
numpy.asanyarray now supports copy and device arguments, matching numpy.asarray.

(gh-26580)
numpy.printoptions, numpy.get_printoptions, and numpy.set_printoptions now support a new option, override_repr, for defining custom repr(array) behavior.

(gh-26611)
numpy.cumulative_sum and numpy.cumulative_prod were added as Array API compatible alternatives for numpy.cumsum and numpy.cumprod. The new functions can include a fixed initial (zeros for sum and ones for prod) in the result.

(gh-26724)
numpy.clip now supports max and min keyword arguments which are meant to replace a_min and a_max. Also, for np.clip(a) or np.clip(a, None, None) a copy of the input array will be returned instead of raising an error.

(gh-26724)
numpy.astype now supports device argument.

(gh-26724)

Improvements

`histogram` auto-binning now returns bin sizes >=1 for integer input data

For integer input data, bin sizes smaller than 1 result in spurious empty bins. This is now avoided when the number of bins is computed using one of the algorithms provided by histogram_bin_edges.

(gh-12150)

`ndarray` shape-type parameter is now covariant and bound to `tuple[int, ...]`

Static typing for ndarray is a long-term effort that continues with this change. It is a generic type with type parameters for the shape and the data type. Previously, the shape type parameter could be any value. This change restricts it to a tuple of ints, as one would expect from using ndarray.shape. Further, the shape-type parameter has been changed from invariant to covariant. This change also applies to the subtypes of ndarray, e.g. numpy.ma.MaskedArray. See the typing docs for more information.

(gh-26081)

`np.quantile` with method `closest_observation` chooses nearest even order statistic

This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations.

(gh-26656)

`lapack_lite` is now thread safe

NumPy provides a minimal low-performance version of LAPACK named lapack_lite that can be used if no BLAS/LAPACK system is detected at build time.

Until now, lapack_lite was not thread safe. Single-threaded use cases did not hit any issues, but running linear algebra operations in multiple threads could lead to errors, incorrect results, or segfaults due to data races.

We have added a global lock, serializing access to lapack_lite in multiple threads.

(gh-26750)

The `numpy.printoptions` context manager is now thread and async-safe

In prior versions of NumPy, the printoptions were defined using a combination of Python and C global variables. We have refactored so the state is stored in a python ContextVar, making the context manager thread and async-safe.

(gh-26846)

Type hinting `numpy.polynomial`

Starting from the 2.1 release, PEP 484 type annotations have been included for the functions and convenience classes in numpy.polynomial and its sub-packages.

(gh-26897)

Improved `numpy.dtypes` type hints

The type annotations for numpy.dtypes are now a better reflection of the runtime: The numpy.dtype type-aliases have been replaced with specialized dtype subtypes, and the previously missing annotations for numpy.dtypes.StringDType have been added.

(gh-27008)

Performance improvements and changes

numpy.save now uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays.

(gh-26388)
OpenBLAS on x86_64 and i686 is built with fewer kernels. Based on benchmarking, there are 5 clusters of performance around these kernels: PRESCOTT NEHALEM SANDYBRIDGE HASWELL SKYLAKEX.

(gh-27147)
OpenBLAS on windows is linked without quadmath, simplifying licensing

(gh-27147)
Due to a regression in OpenBLAS on windows, the performance improvements when using multiple threads for OpenBLAS 0.3.26 were reverted.

(gh-27147)

`ma.cov` and `ma.corrcoef` are now significantly faster

The private function has been refactored along with ma.cov and ma.corrcoef. They are now significantly faster, particularly on large, masked arrays.

(gh-26285)

Changes

As numpy.vecdot is now a ufunc it has a less precise signature. This is due to the limitations of ufunc's typing stub.

(gh-26313)
numpy.floor, numpy.ceil, and numpy.trunc now won't perform casting to a floating dtype for integer and boolean dtype input arrays.

(gh-26766)

`ma.corrcoef` may return a slightly different result

A pairwise observation approach is currently used in ma.corrcoef to calculate the standard deviations for each pair of variables. This has been changed as it is being used to normalise the covariance, estimated using ma.cov, which does not consider the observations for each variable in a pairwise manner, rendering it unnecessary. The normalisation has been replaced by the more appropriate standard deviation for each variable, which significantly reduces the wall time, but will return slightly different estimates of the correlation coefficients in cases where the observations between a pair of variables are not aligned. However, it will return the same estimates in all other cases, including returning the same correlation matrix as corrcoef when using a masked array with no masked values.

(gh-26285)

Cast-safety fixes in `copyto` and `full`

copyto now uses NEP 50 correctly and applies this to its cast safety. Python integer to NumPy integer casts and Python float to NumPy float casts are now considered "safe" even if assignment may fail or precision may be lost. This means the following examples change slightly:

np.copyto(int8_arr, 1000) previously performed an unsafe/same-kind cast of the Python integer. It will now always raise, to achieve an unsafe cast you must pass an array or NumPy scalar.
np.copyto(uint8_arr, 1000, casting="safe") will raise an OverflowError rather than a TypeError due to same-kind casting.
np.copyto(float32_arr, 1e300, casting="safe") will overflow to inf (float32 cannot hold 1e300) rather raising a TypeError.

Further, only the dtype is used when assigning NumPy scalars (or 0-d arrays), meaning that the following behaves differently:

np.copyto(float32_arr, np.float64(3.0), casting="safe") raises.
np.coptyo(int8_arr, np.int64(100), casting="safe") raises. Previously, NumPy checked whether the 100 fits the int8_arr.

This aligns copyto, full, and full_like with the correct NumPy 2 behavior.

(gh-27091)

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`v2.0.2`: NumPy 2.0.2 release (Aug 26, 2024)

Compare Source

NumPy 2.0.2 Release Notes

NumPy 2.0.2 is a maintenance release that fixes bugs and regressions discovered after the 2.0.1 release.

The Python versions supported by this release are 3.9-3.12.

Contributors

A total of 13 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Bruno Oliveira +
Charles Harris
Chris Sidebottom
Christian Heimes +
Christopher Sidebottom
Mateusz Sokół
Matti Picus
Nathan Goldbaum
Pieter Eendebak
Raghuveer Devulapalli
Ralf Gommers
Sebastian Berg
Yair Chuchem +

Pull requests merged

A total of 19 pull requests were merged for this release.

#27000: REL: Prepare for the NumPy 2.0.1 release [wheel build]
#27001: MAINT: prepare 2.0.x for further development
#27021: BUG: cfuncs.py: fix crash when sys.stderr is not available
#27022: DOC: Fix migration note for alltrue and sometrue
#27061: BUG: use proper input and output descriptor in array_assign_subscript...
#27073: BUG: Mirror VQSORT_ENABLED logic in Quicksort
#27074: BUG: Bump Highway to latest master
#27077: BUG: Off by one in memory overlap check
#27122: BUG: Use the new npyv_loadable_stride_ functions for ldexp and...
#27126: BUG: Bump Highway to latest
#27128: BUG: add missing error handling in public_dtype_api.c
#27129: BUG: fix another cast setup in array_assign_subscript
#27130: BUG: Fix building NumPy in FIPS mode
#27131: BLD: update vendored Meson for cross-compilation patches
#27146: MAINT: Scipy openblas 0.3.27.44.4
#27151: BUG: Do not accidentally store dtype metadata in np.save
#27195: REV: Revert undef I and document it
#27213: BUG: Fix NPY_RAVEL_AXIS on backwards compatible NumPy 2 builds
#27279: BUG: Fix array_equal for numeric and non-numeric scalar types

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`v2.0.1`

Compare Source

NumPy 2.0.1 Release Notes

NumPy 2.0.1 is a maintenance release that fixes bugs and regressions discovered after the 2.0.0 release. NumPy 2.0.1 is the last planned release in the 2.0.x series, 2.1.0rc1 should be out shortly.

The Python versions supported by this release are 3.9-3.12.

NOTE: Do not use the GitHub generated "Source code" files listed in the "Assets", they are garbage.

Improvements

`np.quantile` with method `closest_observation` chooses nearest even order statistic

This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations.

(gh-26656)

Contributors

A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

@vahidmech +
Alex Herbert +
Charles Harris
Giovanni Del Monte +
Leo Singer
Lysandros Nikolaou
Matti Picus
Nathan Goldbaum
Patrick J. Roddy +
Raghuveer Devulapalli
Ralf Gommers
Rostan Tabet +
Sebastian Berg
Tyler Reddy
Yannik Wicke +

Pull requests merged

A total of 24 pull requests were merged for this release.

#26711: MAINT: prepare 2.0.x for further development
#26792: TYP: fix incorrect import in ma/extras.pyi stub
#26793: DOC: Mention '1.25' legacy printing mode in set_printoptions
#26794: DOC: Remove mention of NaN and NAN aliases from constants
#26821: BLD: Fix x86-simd-sort build failure on openBSD
#26822: BUG: Ensure output order follows input in numpy.fft
#26823: TYP: fix missing sys import in numeric.pyi
#26832: DOC: remove hack to override _add_newdocs_scalars
#26835: BUG: avoid side-effect of 'include complex.h'
#26836: BUG: fix max_rows and chunked string/datetime reading in loadtxt
#26837: BUG: fix PyArray_ImportNumPyAPI under -Werror=strict-prototypes
#26856: DOC: Update some documentation
#26868: BUG: fancy indexing copy
#26869: BUG: Mismatched allocation domains in PyArray_FillWithScalar
#26870: BUG: Handle --f77flags and --f90flags for meson [wheel build]
#26887: BUG: Fix new DTypes and new string promotion when signature is...
#26888: BUG: remove numpy.f2py from excludedimports
#26959: BUG: Quantile closest_observation to round to nearest even order
#26960: BUG: Fix off-by-one error in amount of characters in strip
#26961: API: Partially revert unique with return_inverse
#26962: BUG,MAINT: Fix utf-8 character stripping memory access
#26963: BUG: Fix out-of-bound minimum offset for in1d table method
#26971: BUG: fix f2py tests to work with v2 API
#26995: BUG: Add object cast to avoid warning with limited API

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`v2.0.0`

Compare Source

NumPy 2.0.0 Release Notes

NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs.

This major release includes breaking changes that could not happen in a regular minor (feature) release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0, in addition to these release notes, include:

The numpy-2-migration-guide
The Numpy 2.0-specific advice in for downstream package authors

Highlights

Highlights of this release include:

New features:
- A new variable-length string dtype, numpy.dtypes.StringDType and a new numpy.strings namespace with performant ufuncs for string operations,
- Support for float32 and longdouble in all numpy.fft functions,
- Support for the array API standard in the main numpy namespace.
Performance improvements:
- Sorting functions sort, argsort, partition, argpartition have been accelerated through the use of the Intel x86-simd-sort and Google Highway libraries, and may see large (hardware-specific) speedups,
- macOS Accelerate support and binary wheels for macOS >=14, with significant performance improvements for linear algebra operations on macOS, and wheels that are about 3 times smaller,
- numpy.char fixed-length string operations have been accelerated by implementing ufuncs that also support numpy.dtypes.StringDType in addition to the fixed-length string dtypes,
- A new tracing and introspection API, numpy.lib.introspect.opt_func_info, to determine which hardware-specific kernels are available and will be dispatched to.
- numpy.save now uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays.
Python API improvements:
- A clear split between public and private API, with a new module structure and each public function now available in a single place.
- Many removals of non-recommended functions and aliases. This should make it easier to learn and use NumPy. The number of objects in the main namespace decreased by ~10% and in numpy.lib by ~80%.
- Canonical dtype names and a new numpy.isdtype` introspection function,
C API improvements:
- A new public C API for creating custom dtypes,
- Many outdated functions and macros removed, and private internals hidden to ease future extensibility,
- New, easier to use, initialization functions: PyArray_ImportNumPyAPI and PyUFunc_ImportUFuncAPI.
Improved behavior:
- Improvements to type promotion behavior was changed by adopting NEP 50. This fixes many user surprises about promotions which previously often depended on data values of input arrays rather than only their dtypes. Please see the NEP and the numpy-2-migration-guide for details as this change can lead to changes in output dtypes and lower precision results for mixed-dtype operations.
- The default integer type on Windows is now int64 rather than int32, matching the behavior on other platforms,
- The maximum number of array dimensions is changed from 32 to 64
Documentation:
- The reference guide navigation was significantly improved, and there is now documentation on NumPy's module structure,
- The building from source documentation was completely rewritten,

Furthermore there are many changes to NumPy internals, including continuing to migrate code from C to C++, that will make it easier to improve and maintain NumPy in the future.

The "no free lunch" theorem dictates that there is a price to pay for all these API and behavior improvements and better future extensibility. This price is:

Backwards compatibility. There are a significant number of breaking changes to both the Python and C APIs. In the majority of cases, there are clear error messages that will inform the user how to adapt their code. However, there are also changes in behavior for which it was not possible to give such an error message - these cases are all covered in the Deprecation and Compatibility sections below, and in the numpy-2-migration-guide.

Note that there is a ruff mode to auto-fix many things in Python code.
Breaking changes to the NumPy ABI. As a result, binaries of packages that use the NumPy C API and were built against a NumPy 1.xx release will not work with NumPy 2.0. On import, such packages will see an ImportError with a message about binary incompatibility.

It is possible to build binaries against NumPy 2.0 that will work at runtime with both NumPy 2.0 and 1.x. See numpy-2-abi-handling for more details.

All downstream packages that depend on the NumPy ABI are advised to do a new release built against NumPy 2.0 and verify that that release works with both 2.0 and 1.26 - ideally in the period between 2.0.0rc1 (which will be ABI-stable) and the final 2.0.0 release to avoid problems for their users.

The Python versions supported by this release are 3.9-3.12.

NumPy 2.0 Python API removals

np.geterrobj, np.seterrobj and the related ufunc keyword argument extobj= have been removed. The preferred replacement for all of these is using the context manager with np.errstate():.

(gh-23922)
np.cast has been removed. The literal replacement for np.cast[dtype](arg) is np.asarray(arg, dtype=dtype).
np.source has been removed. The preferred replacement is inspect.getsource.
np.lookfor has been removed.

(gh-24144)
numpy.who has been removed. As an alternative for the removed functionality, one can use a variable explorer that is available in IDEs such as Spyder or Jupyter Notebook.

(gh-24321)
Warnings and exceptions present in numpy.exceptions, e.g, numpy.exceptions.ComplexWarning, numpy.exceptions.VisibleDeprecationWarning, are no longer exposed in the main namespace.
Multiple niche enums, expired members and functions have been removed from the main namespace, such as: ERR_*, SHIFT_*, np.fastCopyAndTranspose, np.kernel_version, np.numarray, np.oldnumeric and np.set_numeric_ops.

(gh-24316)
Replaced from ... import * in the numpy/__init__.py with explicit imports. As a result, these main namespace members got removed: np.FLOATING_POINT_SUPPORT, np.FPE_*, np.NINF, np.PINF, np.NZERO, np.PZERO, np.CLIP, np.WRAP, np.WRAP, np.RAISE, np.BUFSIZE, np.UFUNC_BUFSIZE_DEFAULT, np.UFUNC_PYVALS_NAME, np.ALLOW_THREADS, np.MAXDIMS, np.MAY_SHARE_EXACT, np.MAY_SHARE_BOUNDS, add_newdoc, np.add_docstring and np.add_newdoc_ufunc.

(gh-24357)
Alias np.float_ has been removed. Use np.float64 instead.
Alias np.complex_ has been removed. Use np.complex128 instead.
Alias np.longfloat has been removed. Use np.longdouble instead.
Alias np.singlecomplex has been removed. Use np.complex64 instead.
Alias np.cfloat has been removed. Use np.complex128 instead.
Alias np.longcomplex has been removed. Use np.clongdouble instead.
Alias np.clongfloat has been removed. Use np.clongdouble instead.
Alias np.string_ has been removed. Use np.bytes_ instead.
Alias np.unicode_ has been removed. Use np.str_ instead.
Alias np.Inf has been removed. Use np.inf instead.
Alias np.Infinity has been removed. Use np.inf instead.
Alias np.NaN has been removed. Use np.nan instead.
Alias np.infty has been removed. Use np.inf instead.
Alias np.mat has been removed. Use np.asmatrix instead.
np.issubclass_ has been removed. Use the issubclass builtin instead.
np.asfarray has been removed. Use np.asarray with a proper dtype instead.
np.set_string_function has been removed. Use np.set_printoptions instead with a formatter for custom printing of NumPy objects.
np.tracemalloc_domain is now only available from np.lib.
np.recfromcsv and recfromtxt are now only available from np.lib.npyio.
np.issctype, np.maximum_sctype, np.obj2sctype, np.sctype2char, np.sctypes, np.issubsctype were all removed from the main namespace without replacement, as they where niche members.
Deprecated np.deprecate and np.deprecate_with_doc has been removed from the main namespace. Use DeprecationWarning instead.
Deprecated np.safe_eval has been removed from the main namespace. Use ast.literal_eval instead.

(gh-24376)
np.find_common_type has been removed. Use numpy.promote_types or numpy.result_type instead. To achieve semantics for the scalar_types argument, use numpy.result_type and pass 0, 0.0, or 0j as a Python scalar instead.
np.round_ has been removed. Use np.round instead.
np.nbytes has been removed. Use np.dtype(<dtype>).itemsize instead.

(gh-24477)
np.compare_chararrays has been removed from the main namespace. Use np.char.compare_chararrays instead.
The charrarray in the main namespace has been deprecated. It can be imported without a deprecation warning from np.char.chararray for now, but we are planning to fully deprecate and remove chararray in the future.
np.format_parser has been removed from the main namespace. Use np.rec.format_parser instead.

(gh-24587)
Support for seven data type string aliases has been removed from np.dtype: int0, uint0, void0, object0, str0, bytes0 and bool8.

(gh-24807)
The experimental numpy.array_api submodule has been removed. Use the main numpy namespace for regular usage instead, or the separate array-api-strict package for the compliance testing use case for which numpy.array_api was mostly used.

(gh-25911)

`__array_prepare__` is removed

UFuncs called __array_prepare__ before running computations for normal ufunc calls (not generalized ufuncs, reductions, etc.). The function was also called instead of __array_wrap__ on the results of some linear algebra functions.

It is now removed. If you use it, migrate to __array_ufunc__ or rely on __array_wrap__ which is called with a context in all cases, although only after the result array is filled. In those code paths, __array_wrap__ will now be passed a base class, rather than a subclass array.

(gh-25105)

Deprecations

np.compat has been deprecated, as Python 2 is no longer supported.
numpy.int8 and similar classes will no longer support conversion of out of bounds python integers to integer arrays. For example, conversion of 255 to int8 will not return -1. numpy.iinfo(dtype) can be used to check the machine limits for data types. For example, np.iinfo(np.uint16) returns min = 0 and max = 65535.

np.array(value).astype(dtype) will give the desired result.
np.safe_eval has been deprecated. ast.literal_eval should be used instead.

(gh-23830)
np.recfromcsv, np.recfromtxt, np.disp, np.get_array_wrap, np.maximum_sctype, np.deprecate and np.deprecate_with_doc have been deprecated.

(gh-24154)
np.trapz has been deprecated. Use np.trapezoid or a scipy.integrate function instead.
np.in1d has been deprecated. Use np.isin instead.
Alias np.row_stack has been deprecated. Use np.vstack directly.

(gh-24445)
__array_wrap__ is now passed arr, context, return_scalar and support for implementations not accepting all three are deprecated. Its signature should be __array_wrap__(self, arr, context=None, return_scalar=False)

(gh-25409)
Arrays of 2-dimensional vectors for np.cross have been deprecated. Use arrays of 3-dimensional vectors instead.

(gh-24818)
np.dtype("a") alias for np.dtype(np.bytes_) was deprecated. Use np.dtype("S") alias instead.

(gh-24854)
Use of keyword arguments x and y with functions assert_array_equal and assert_array_almost_equal has been deprecated. Pass the first two arguments as positional arguments instead.

(gh-24978)

`numpy.fft` deprecations for n-D transforms with None values in arguments

Using fftn, ifftn, rfftn, irfftn, fft2, ifft2, rfft2 or irfft2 with the s parameter set to a value that is not None and the axes parameter set to None has been deprecated, in line with the array API standard. To retain current behaviour, pass a sequence [0, ..., k-1] to axes for an array of dimension k.

Furthermore, passing an array to s which contains None values is deprecated as the parameter is documented to accept a sequence of integers in both the NumPy docs and the array API specification. To use the default behaviour of the corresponding 1-D transform, pass the value matching the default for its n parameter. To use the default behaviour for every axis, the s argument can be omitted.

(gh-25495)

`np.linalg.lstsq` now defaults to a new `rcond` value

numpy.linalg.lstsq now uses the new rcond value of the machine precision times max(M, N). Previously, the machine precision was used but a FutureWarning was given to notify that this change will happen eventually. That old behavior can still be achieved by passing rcond=-1.

(gh-25721)

Expired deprecations

The np.core.umath_tests submodule has been removed from the public API. (Deprecated in NumPy 1.15)

(gh-23809)
The PyDataMem_SetEventHook deprecation has expired and it is removed. Use tracemalloc and the np.lib.tracemalloc_domain domain. (Deprecated in NumPy 1.23)

(gh-23921)
The deprecation of set_numeric_ops and the C functions PyArray_SetNumericOps and PyArray_GetNumericOps has been expired and the functions removed. (Deprecated in NumPy 1.16)

(gh-23998)
The fasttake, fastclip, and fastputmask ArrFuncs deprecation is now finalized.
The deprecated function fastCopyAndTranspose and its C counterpart are now removed.
The deprecation of PyArray_ScalarFromObject is now finalized.

(gh-24312)
np.msort has been removed. For a replacement, np.sort(a, axis=0) should be used instead.

(gh-24494)
np.dtype(("f8", 1) will now return a shape 1 subarray dtype rather than a non-subarray one.

(gh-25761)
Assigning to the .data attribute of an ndarray is disallowed and will raise.
np.binary_repr(a, width) will raise if width is too small.
Using NPY_CHAR in PyArray_DescrFromType() will raise, use NPY_STRING NPY_UNICODE, or NPY_VSTRING instead.

(gh-25794)

Compatibility notes

`loadtxt` and `genfromtxt` default encoding changed

loadtxt and genfromtxt now both default to encoding=None which may mainly modify how converters work. These will now be passed str rather than bytes. Pass the encoding explicitly to always get the new or old behavior. For genfromtxt the change also means that returned values will now be unicode strings rather than bytes.

(gh-25158)

`f2py` compatibility notes

f2py will no longer accept ambiguous -m and .pyf CLI combinations. When more than one .pyf file is passed, an error is raised. When both -m and a .pyf is passed, a warning is emitted and the -m provided name is ignored.

(gh-25181)
The f2py.compile() helper has been removed because it leaked memory, has been marked as experimental for several years now, and was implemented as a thin subprocess.run wrapper. It was also one of the test bottlenecks. See gh-25122 for the full rationale. It also used several np.distutils features which are too fragile to be ported to work with meson.
Users are urged to replace calls to f2py.compile with calls to subprocess.run("python", "-m", "numpy.f2py",... instead, and to use environment variables to interact with meson. Native files are also an option.

(gh-25193)

Minor changes in behavior of sorting functions

Due to algorithmic changes and use of SIMD code, sorting functions with methods that aren't stable may return slightly different results in 2.0.0 compared to 1.26.x. This includes the default method of numpy.argsort and numpy.argpartition.

Removed ambiguity when broadcasting in `np.solve`

The broadcasting rules for np.solve(a, b) were ambiguous when b had 1 fewer dimensions than a. This has been resolved in a backward-incompatible way and is now compliant with the Array API. The old behaviour can be reconstructed by using np.solve(a, b[..., None])[..., 0].

(gh-25914)

Modified representation for `Polynomial`

The representation method for numpy.polynomial.polynomial.Polynomial was updated to include the domain in the representation. The plain text and latex representations are now consistent. For example the output of str(np.polynomial.Polynomial([1, 1], domain=[.1, .2])) used to be 1.0 + 1.0 x, but now is 1.0 + 1.0 (-3.0000000000000004 + 20.0 x).

(gh-21760)

C API changes

The PyArray_CGT, PyArray_CLT, PyArray_CGE, PyArray_CLE, PyArray_CEQ, PyArray_CNE macros have been removed.
PyArray_MIN and PyArray_MAX have been moved from ndarraytypes.h to npy_math.h.

(gh-24258)
A C API for working with numpy.dtypes.StringDType arrays has been exposed. This includes functions for acquiring and releasing mutexes which lock access to the string data, as well as packing and unpacking UTF-8 bytestreams from array entries.
NPY_NTYPES has been renamed to NPY_NTYPES_LEGACY as it does not include new NumPy built-in DTypes. In particular the new string DType will likely not work correctly with code that handles legacy DTypes.

(gh-25347)
The C-API now only exports the static inline function versions of the array accessors (previously this depended on using "deprecated API"). While we discourage it, the struct fields can still be used directly.

(gh-25789)
NumPy now defines PyArray_Pack to set an individual memory address. Unlike PyArray_SETITEM this function is equivalent to setting an individual array item and does not require a NumPy array input.

(gh-25954)
The ->f slot has been removed from PyArray_Descr. If you use this slot, replace accessing it with PyDataType_GetArrFuncs (see its documentation and the numpy-2-migration-guide). In some cases using other functions like PyArray_GETITEM may be an alternatives.
PyArray_GETITEM and PyArray_SETITEM now require the import of the NumPy API table to be used and are no longer defined in ndarraytypes.h.

(gh-25812)
Due to runtime dependencies, the definition for functionality accessing the dtype flags was moved from numpy/ndarraytypes.h and is only available after including numpy/ndarrayobject.h as it requires import_array(). This includes PyDataType_FLAGCHK, PyDataType_REFCHK and NPY_BEGIN_THREADS_DESCR.
The dtype flags on PyArray_Descr must now be accessed through the PyDataType_FLAGS inline function to be compatible with both 1.x and 2.x. This function is defined in npy_2_compat.h to allow backporting. Most or all users should use PyDataType_FLAGCHK which is available on 1.x and does not require backporting. Cython users should use Cython 3. Otherwise access will go through Python unless they use PyDataType_FLAGCHK instead.

(gh-25816)

Datetime functionality exposed in the C API and Cython bindings

The functions NpyDatetime_ConvertDatetime64ToDatetimeStruct, NpyDatetime_ConvertDatetimeStructToDatetime64, NpyDatetime_ConvertPyDateTimeToDatetimeStruct, NpyDatetime_GetDatetimeISO8601StrLen, NpyDatetime_MakeISO8601Datetime, and NpyDatetime_ParseISO8601Datetime have been added to the C API to facilitate converting between strings, Python datetimes, and NumPy datetimes in external libraries.

(gh-21199)

Const correctness for the generalized ufunc C API

The NumPy C API's functions for constructing generalized ufuncs (PyUFunc_FromFuncAndData, PyUFunc_FromFuncAndDataAndSignature, PyUFunc_FromFuncAndDataAndSignatureAndIdentity) take types and data arguments that are not modified by NumPy's internals. Like the name and doc arguments, third-party Python extension modules are likely to supply these arguments from static constants. The types and data arguments are now const-correct: they are declared as const char *types and void *const *data, respectively. C code should not be affected, but C++ code may be.

(gh-23847)

Larger `NPY_MAXDIMS` and `NPY_MAXARGS`, `NPY_RAVEL_AXIS` introduced

NPY_MAXDIMS is now 64, you may want to review its use. This is usually used in a stack allocation, where the increase should be safe. However, we do encourage generally to remove any use of NPY_MAXDIMS and NPY_MAXARGS to eventually allow removing the constraint completely. For the conversion helper and C-API functions mirroring Python ones such as take, NPY_MAXDIMS was used to mean axis=None. Such usage must be replaced with NPY_RAVEL_AXIS. See also migration_maxdims.

(gh-25149)

`NPY_MAXARGS` not constant and `PyArrayMultiIterObject` size change

Since NPY_MAXARGS was increased, it is now a runtime constant and not compile-time constant anymore. We expect almost no users to notice this. But if used for stack allocations it now must be replaced with a custom constant using NPY_MAXARGS as an additional runtime check.

The sizeof(PyArrayMultiIterObject) no longer includes the full size of the object. We expect nobody to notice this change. It was necessary to avoid issues with Cython.

(gh-25271)

Required changes for custom legacy user dtypes

In order to improve our DTypes it is unfortunately necessary to break the ABI, which requires some changes for dtypes registered with PyArray_RegisterDataType. Please see the documentation of PyArray_RegisterDataType for how to adapt your code and achieve compatibility with both 1.x and 2.x.

(gh-25792)

New Public DType API

The C implementation of the NEP 42 DType API is now public. While the DType API has shipped in NumPy for a few versions, it was only usable in sessions with a special environment variable set. It is now possible to write custom DTypes outside of NumPy using the new DType API and the normal import_array() mechanism for importing the numpy C API.

See dtype-api for more details about the API. As always with a new feature, please report any bugs you run into implementing or using a new DType. It is likely that downstream C code that works with dtypes will need to be updated to work correctly with new DTypes.

(gh-25754)

New C-API import functions

We have now added PyArray_ImportNumPyAPI and PyUFunc_ImportUFuncAPI as static inline functions to import the NumPy C-API tables. The new functions have two advantages over import_array and import_ufunc:

They check whether the import was already performed and are light-weight if not, allowing to add them judiciously (although this is not preferable in most cases).
The old mechanisms were macros rather than functions which included a return statement.

The PyArray_ImportNumPyAPI() function is included in npy_2_compat.h for simpler backporting.

(gh-25866)

Structured dtype information access through functions

The dtype structures fields c_metadata, names, fields, and subarray must now be accessed through new functions following the same names, such as PyDataType_NAMES. Direct access of the fields is not valid as they do not exist for all PyArray_Descr instances. The metadata field is kept, but the macro version should also be preferred.

(gh-25802)

Descriptor `elsize` and `alignment` access

Unless compiling only with NumPy 2 support, the elsize and aligment fields must now be accessed via PyDataType_ELSIZE, PyDataType_SET_ELSIZE, and PyDataType_ALIGNMENT. In cases where the descriptor is attached to an array, we advise using PyArray_ITEMSIZE as it exists on all NumPy versions. Please see migration_c_descr for more information.

(gh-25943)

NumPy 2.0 C API removals

npy_interrupt.h and the corresponding macros like NPY_SIGINT_ON have been removed. We recommend querying PyErr_CheckSignals() or PyOS_InterruptOccurred() periodically (these do currently require holding the GIL though).
The noprefix.h header has been removed. Replace missing symbols with their prefixed counterparts (usually an added NPY_ or npy_).

(gh-23919)
PyUFunc_GetPyVals, PyUFunc_handlefperr, and PyUFunc_checkfperr have been removed. If needed, a new backwards compatible function to raise floating point errors could be restored. Reason for removal: there are no known users and the functions would have made with np.errstate() fixes much more difficult).

(gh-23922)
The numpy/old_defines.h which was part of the API deprecated since NumPy 1.7 has been removed. This removes macros of the form PyArray_CONSTANT. The replace_old_macros.sed script may be useful to convert them to the NPY_CONSTANT version.

(gh-24011)
The legacy_inner_loop_selector member of the ufunc struct is removed to simplify improvements to the dispatching system. There are no known users overriding or directly accessing this member.

(gh-24271)
NPY_INTPLTR has been removed to avoid confusion (see intp redefinition).

(gh-24888)
The advanced indexing MapIter and related API has been removed. The (truly) public part of it was not well tested and had only one known user (Theano). Making it private will simplify improvements to speed up ufunc.at, make advanced indexing more maintainable, and was important for increasing the maximum number of dimensions of arrays to 64. Please let us know if this API is important to you so we can find a solution together.

(gh-25138)
The NPY_MAX_ELSIZE macro has been removed, as it only ever reflected builtin numeric types and served no internal purpose.

(gh-25149)
PyArray_REFCNT and NPY_REFCOUNT are removed. Use Py_REFCNT instead.

(gh-25156)
PyArrayFlags_Type and PyArray_NewFlagsObject as well as PyArrayFlagsObject are private now. There is no known use-case; use the Python API if needed.
PyArray_MoveInto, PyArray_CastTo, PyArray_CastAnyTo are removed use PyArray_CopyInto and if absolutely needed PyArray_CopyAnyInto (the latter does a flat copy).
PyArray_FillObjectArray is removed, its only true use was for implementing np.empty. Create a new empty array or use PyArray_FillWithScalar() (decrefs existing objects).
PyArray_CompareUCS4 and PyArray_CompareString are removed. Use the standard C string comparison functions.
PyArray_ISPYTHON is removed as it is misleading, has no known use-cases, and is easy to replace.
PyArray_FieldNames is removed, as it is unclear what it would be useful for. It also has incorrect semantics in some possible use-cases.
PyArray_TypestrConvert is removed, since it seems a misnomer and unlikely to be used by anyone. If you know the size or are limited to few types, just use it explicitly, otherwise go via Python strings.

(gh-25292)
PyDataType_GetDatetimeMetaData is removed, it did not actually do anything since at least NumPy 1.7.

(gh-25802)
PyArray_GetCastFunc is removed. Note that custom legacy user dtypes can still provide a castfunc as their implementation, but any access to them is now removed. The reason for this is that NumPy never used these internally for many years. If you use simple numeric types, please just use C casts directly. In case you require an alternative, please let us know so we can create new API such as PyArray_CastBuffer() which could use old or new cast functions depending on the NumPy version.

(gh-25161)

New Features

`np.add` was extended to work with `unicode` and `bytes` dtypes.

(gh-24858)

A new `bitwise_count` function

This new function counts the number of 1-bits in a number. numpy.bitwise_count works on all the numpy integer types and integer-like objects.

>>> a = np.array([2**i - 1 for i in range(16)])
>>> np.bitwise_count(a)
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15],
      dtype=uint8)

(gh-19355)

macOS Accelerate support, including the ILP64

Support for the updated Accelerate BLAS/LAPACK library, including ILP64 (64-bit integer) support, in macOS 13.3 has been added. This brings arm64 support, and significant performance improvements of up to 10x for commonly used linear algebra operations. When Accelerate is selected at build time, or if no explicit BLAS library selection is done, the 13.3+ version will automatically be used if available.

(gh-24053)

Binary wheels are also available. On macOS >=14.0, users who install NumPy from PyPI will get wheels built against Accelerate rather than OpenBLAS.

(gh-25255)

Option to use weights for quantile and percentile functions

A weights keyword is now available for numpy.quantile, numpy.percentile, numpy.nanquantile and numpy.nanpercentile. Only method="inverted_cdf" supports weights.

(gh-24254)

Improved CPU optimization tracking

A new tracer mechanism is available which enables tracking of the enabled targets for each optimized function (i.e., that uses hardware-specific SIMD instructions) in the NumPy library. With this enhancement, it becomes possible to precisely monitor the enabled CPU dispatch targets for the dispatched functions.

A new function named opt_func_info has been added to the new namespace numpy.lib.introspect, offering this tracing capability. This function allows you to retrieve information about the enabled targets based on function names and data type signatures.

(gh-24420)

A new Meson backend for `f2py`

f2py in compile mode (i.e. f2py -c) now accepts the --backend meson option. This is the default option for Python >=3.12. For older Python versions, f2py will still default to --backend distutils.

To support this in realistic use-cases, in compile mode f2py takes a --dep flag one or many times which maps to dependency() calls in the meson backend, and does nothing in the distutils backend.

There are no changes for users of f2py only as a code generator, i.e. without -c.

(gh-24532)

`bind(c)` support for `f2py`

Both functions and subroutines can be annotated with bind(c). f2py will handle both the correct type mapping, and preserve the unique label for other C interfaces.

Note: bind(c, name = 'routine_name_other_than_fortran_routine') is not honored by the f2py bindings by design, since bind(c) with the name is meant to guarantee only the same name in C and Fortran, not in Python and Fortran.

(gh-24555)

A new `strict` option for several testing functions

The strict keyword is now available for numpy.testing.assert_allclose, numpy.testing.assert_equal, and numpy.testing.assert_array_less. Setting strict=True will disable the broadcasting behaviour for scalars and ensure that input arrays have the same data type.

(gh-24680, gh-24770, gh-24775)

Add `np.core.umath.find` and `np.core.umath.rfind` UFuncs

Add two find and rfind UFuncs that operate on unicode or byte strings and are used in np.char. They operate similar to str.find and str.rfind.

(gh-24868)

`diagonal` and `trace` for `numpy.linalg`

numpy.linalg.diagonal and numpy.linalg.trace have been added, which are array API standard-compatible variants of numpy.diagonal and numpy.trace. They differ in the default axis selection which define 2-D sub-arrays.

(gh-24887)

New `long` and `ulong` dtypes

numpy.long and numpy.ulong have been added as NumPy integers mapping to C's long and unsigned long. Prior to NumPy 1.24, numpy.long was an alias to Python's int.

(gh-24922)

`svdvals` for `numpy.linalg`

numpy.linalg.svdvals has been added. It computes singular values for (a stack of) matrices. Executing np.svdvals(x) is the same as calling np.svd(x, compute_uv=False, hermitian=False). This function is compatible with the array API standard.

(gh-24940)

A new `isdtype` function

numpy.isdtype was added to provide a canonical way to classify NumPy's dtypes in compliance with the array API standard.

(gh-25054)

A new `astype` function

numpy.astype was added to provide an array API standard-compatible alternative to the numpy.ndarray.astype method.

(gh-25079)

Array API compatible functions' aliases

13 aliases for existing functions were added to improve compatibility with the array API standard:

Trigonometry: acos, acosh, asin, asinh, atan, atanh, atan2.
Bitwise: bitwise_left_shift, bitwise_invert, bitwise_right_shift.
Misc: concat, permute_dims, pow.
In numpy.linalg: tensordot, matmul.

(gh-25086)

New `unique_*` functions

The numpy.unique_all, numpy.unique_counts, numpy.unique_inverse, and numpy.unique_values functions have been added. They provide functionality of numpy.unique with different sets of flags. They are array API standard-compatible, and because the number of arrays they return does not depend on the values of input arguments, they are easier to target for JIT compilation.

(gh-25088)

Matrix transpose support for ndarrays

NumPy now offers support for calculating the matrix transpose of an array (or stack of arrays). The matrix transpose is equivalent to swapping the last two axes of an array. Both np.ndarray and np.ma.MaskedArray now expose a .mT attribute, and there is a matching new numpy.matrix_transpose function.

(gh-23762)

Array API compatible functions for `numpy.linalg`

Six new functions and two aliases were added to improve compatibility with the Array API standard for `numpy.linalg`:

numpy.linalg.matrix_norm - Computes the matrix norm of a matrix (or a stack of matrices).
numpy.linalg.vector_norm - Computes the vector norm of a vector (or batch of vectors).
numpy.vecdot - Computes the (vector) dot product of two arrays.
numpy.linalg.vecdot - An alias for numpy.vecdot.
numpy.linalg.matrix_transpose - An alias for numpy.matrix_transpose.

(gh-25155)
numpy.linalg.outer has been added. It computes the outer product of two vectors. It differs from numpy.outer by accepting one-dimensional arrays only. This function is compatible with the array API standard.

(gh-25101)
numpy.linalg.cross has been added. It computes the cross product of two (arrays of) 3-dimensional vectors. It differs from numpy.cross by accepting three-dimensional vectors only. This function is compatible with the array API standard.

(gh-25145)

A `correction` argument for `var` and `std`

A correction argument was added to numpy.var and numpy.std, which is an array API standard compatible alternative to ddof. As both arguments serve a similar purpose, only one of them can be provided at the same time.

(gh-25169)

`ndarray.device` and `ndarray.to_device`

An ndarray.device attribute and ndarray.to_device method were added to numpy.ndarray for array API standard compatibility.

Additionally, device keyword-only arguments were added to: numpy.asarray, numpy.arange, numpy.empty, numpy.empty_like, numpy.eye, numpy.full, numpy.full_like, numpy.linspace, numpy.ones, numpy.ones_like, numpy.zeros, and numpy.zeros_like.

For all these new arguments, only device="cpu" is supported.

(gh-25233)

StringDType has been added to NumPy

We have added a new variable-width UTF-8 encoded string data type, implementing a "NumPy array of Python strings", including support for a user-provided missing data sentinel. It is intended as a drop-in replacement for arrays of Python strings and missing data sentinels using the object dtype. See NEP 55 and the documentation of stringdtype for more details.

(gh-25347)

New keywords for `cholesky` and `pinv`

The upper and rtol keywords were added to numpy.linalg.cholesky and numpy.linalg.pinv, respectively, to improve array API standard compatibility.

For numpy.linalg.pinv, if neither rcond nor rtol is specified, the rcond's default is used. We plan to deprecate and remove rcond in the future.

(gh-25388)

New keywords for `sort`, `argsort` and `linalg.matrix_rank`

New keyword parameters were added to improve array API standard compatibility:

rtol was added to numpy.linalg.matrix_rank.
stable was added to numpy.sort and numpy.argsort.

(gh-25437)

New `numpy.strings` namespace for string ufuncs

NumPy now implements some string operations as ufuncs. The old np.char namespace is still available, and where possible the string manipulation functions in that namespace have been updated to use the new ufuncs, substantially improving their performance.

Where possible, we suggest updating code to use functions in np.strings instead of np.char. In the future we may deprecate np.char in favor of np.strings.

(gh-25463)

`numpy.fft` support for different precisions and in-place calculations

The various FFT routines in numpy.fft now do their calculations natively in float, double, or long double precision, depending on the input precision, instead of always calculating in double precision. Hence, the calculation will now be less precise for single and more precise for long double precision. The data type of the output array will now be adjusted accordingly.

Furthermore, all FFT routines have gained an out argument that can be used for in-place calculations.

(gh-25536)

configtool and pkg-config support

A new numpy-config CLI script is available that can be queried for the NumPy version and for compile flags needed to use the NumPy C API. This will allow build systems to better support the use of NumPy as a dependency. Also, a numpy.pc pkg-config file is now included with Numpy. In order to find its location for use with PKG_CONFIG_PATH, use numpy-config --pkgconfigdir.

(gh-25730)

Array API standard support in the main namespace

The main numpy namespace now supports the array API standard. See array-api-standard-compatibility for details.

(gh-25911)

Improvements

Strings are now supported by `any`, `all`, and the logical ufuncs.

(gh-25651)

Integer sequences as the shape argument for `memmap`

numpy.memmap can now be created with any integer sequence as the shape argument, such as a list or numpy array of integers. Previously, only the types of tuple and int could be used without raising an error.

(gh-23729)

`errstate` is now faster and context safe

The numpy.errstate context manager/decorator is now faster and safer. Previously, it was not context safe and had (rare) issues with thread-safety.

(gh-23936)

AArch64 quicksort speed improved by using Highway's VQSort

The first introduction of the Google Highway library, using VQSort on AArch64. Execution time is improved by up to 16x in some cases, see the MR for benchmark results. Extensions to other platforms will be done in the future.

(gh-24018)

Complex types - underlying C type changes

The underlying C types for all of NumPy's complex types have been changed to use C99 complex types.
While this change does not affect the memory layout of complex types, it changes the API to be used to directly retrieve or write the real or complex part of the complex number, since direct field access (as in c.real or c.imag) is no longer an option. You can now use utilities provided in numpy/npy_math.h to do these operations, like this:
```
npy_cdouble c;
npy_csetreal(&c, 1.0);
npy_csetimag(&c, 0.0);
printf("%d + %di\n", npy_creal(c), npy_cimag(c));
```
To ease cross-version compatibility, equivalent macros and a compatibility layer have been added which can be used by downstream packages to continue to support both NumPy 1.x and 2.x. See complex-numbers for more info.
numpy/npy_common.h now includes complex.h, which means that complex is now a reserved keyword.

(gh-24085)

`iso_c_binding` support and improved common blocks for `f2py`

Previously, users would have to define their own custom f2cmap file to use type mappings defined by the Fortran2003 iso_c_binding intrinsic module. These type maps are now natively supported by f2py

(gh-24555)

f2py now handles common blocks which have kind specifications from modules. This further expands the usability of intrinsics like iso_fortran_env and iso_c_binding.

(gh-25186)

Call `str` automatically on third argument to functions like `assert_equal`

The third argument to functions like numpy.testing.assert_equal now has str called on it automatically. This way it mimics the built-in assert statement, where assert_equal(a, b, obj) works like assert a == b, obj.

(gh-24877)

Support for array-like `atol`/`rtol` in `isclose`, `allclose`

The keywords atol and rtol in numpy.isclose and numpy.allclose now accept both scalars and arrays. An array, if given, must broadcast to the shapes of the first two array arguments.

(gh-24878)

Consistent failure messages in test functions

Previously, some numpy.testing assertions printed messages that referred to the actual and desired results as x and y. Now, these values are consistently referred to as ACTUAL and DESIRED.

(gh-24931)

n-D FFT transforms allow `s[i] == -1`

The numpy.fft.fftn, numpy.fft.ifftn, numpy.fft.rfftn, numpy.fft.irfftn, numpy.fft.fft2, numpy.fft.ifft2, numpy.fft.rfft2 and numpy.fft.irfft2 functions now use the whole input array along the axis i if s[i] == -1, in line with the array API standard.

(gh-25495)

Guard PyArrayScalar_VAL and PyUnicodeScalarObject for the limited API

PyUnicodeScalarObject holds a PyUnicodeObject, which is not available when using Py_LIMITED_API. Add guards to hide it and consequently also make the PyArrayScalar_VAL macro hidden.

(gh-25531)

Changes

np.gradient() now returns a tuple rather than a list making the return value immutable.

(gh-23861)
Being fully context and thread-safe, np.errstate can only be entered once now.
np.setbufsize is now tied to np.errstate(): leaving an np.errstate context will also reset the bufsize.

(gh-23936)
A new public np.lib.array_utils submodule has been introduced and it currently contains three functions: byte_bounds (moved from np.lib.utils), normalize_axis_tuple and normalize_axis_index.

(gh-24540)
Introduce numpy.bool as the new canonical name for NumPy's boolean dtype, and make numpy.bool\_ an alias to it. Note that until NumPy 1.24, np.bool was an alias to Python's builtin bool. The new name helps with array API standard compatibility and is a more intuitive name.

(gh-25080)
The dtype.flags value was previously stored as a signed integer. This means that the aligned dtype struct flag lead to negative flags being set (-128 rather than 128). This flag is now stored unsigned (positive). Code which checks flags manually may need to adapt. This may include code compiled with Cython 0.29.x.

(gh-25816)

Representation of NumPy scalars changed

As per NEP 51, the scalar representation has been updated to include the type information to avoid confusion with Python scalars.

Scalars are now printed as np.float64(3.0) rather than just 3.0. This may disrupt workflows that store representations of numbers (e.g., to files) making it harder to read them. They should be stored as explicit strings, for example by using str() or f"{scalar!s}". For the time being, affected users can use np.set_printoptions(legacy="1.25") to get the old behavior (with possibly a few exceptions). Documentation of downstream projects may require larger updates, if code snippets are tested. We are working on tooling for doctest-plus to facilitate updates.

(gh-22449)

Truthiness of NumPy strings changed

NumPy strings previously were inconsistent about how they defined if the string is True or False and the definition did not match the one used by Python. Strings are now considered True when they are non-empty and False when they are empty. This changes the following distinct cases:

Casts from string to boolean were previously roughly equivalent to string_array.astype(np.int64).astype(bool), meaning that only valid integers could be cast. Now a string of "0" will be considered True since it is not empty. If you need the old behavior, you may use the above step (casting to integer first) or string_array == "0" (if the input is only ever 0 or 1). To get the new result on old NumPy versions use string_array != "".
np.nonzero(string_array) previously ignored whitespace so that a string only containing whitespace was considered False. Whitespace is now considered True.

This change does not affect np.loadtxt, np.fromstring, or np.genfromtxt. The first two still use the integer definition, while genfromtxt continues to match for "true" (ignoring case). However, if np.bool_ is used as a converter the result will change.

The change does affect np.fromregex as it uses direct assignments.

(gh-23871)

A `mean` keyword was added to var and std function

Often when the standard deviation is needed the mean is also needed. The same holds for the variance and the mean. Until now the mean is then calculated twice, the change introduced here for the numpy.var and numpy.std functions allows for passing in a precalculated mean as an keyword argument. See the docstrings for details and an example illustrating the speed-up.

(gh-24126)

Remove datetime64 deprecation warning when constructing with timezone

The numpy.datetime64 method now issues a UserWarning rather than a DeprecationWarning whenever a timezone is included in the datetime string that is provided.

(gh-24193)

Default integer dtype is now 64-bit on 64-bit Windows

The default NumPy integer is now 64-bit on all 64-bit systems as the historic 32-bit default on Windows was a common source of issues. Most users should not notice this. The main issues may occur with code interfacing with libraries written in a compiled language like C. For more information see migration_windows_int64.

(gh-24224)

Renamed `numpy.core` to `numpy._core`

Accessing numpy.core now emits a DeprecationWarning. In practice we have found that most downstream usage of numpy.core was to access functionality that is available in the main numpy namespace. If for some reason you are using functionality in numpy.core that is not available in the main numpy namespace, this means you are likely using private NumPy internals. You can still access these internals via numpy._core without a deprecation warning but we do not provide any backward compatibility guarantees for NumPy internals. Please open an issue if you think a mistake was made and something needs to be made public.

(gh-24634)

The "relaxed strides" debug build option, which was previously enabled through the NPY_RELAXED_STRIDES_DEBUG environment variable or the -Drelaxed-strides-debug config-settings flag has been removed.

(gh-24717)

Redefinition of `np.intp`/`np.uintp` (almost never a change)

Due to the actual use of these types almost always matching the use of size_t/Py_ssize_t this is now the definition in C. Previously, it matched intptr_t and uintptr_t which would often have been subtly incorrect. This has no effect on the vast majority of machines since the size of these types only differ on extremely niche platforms.

However, it means that:

Pointers may not necessarily fit into an intp typed array anymore. The p and P character codes can still be used, however.
Creating intptr_t or uintptr_t typed arrays in C remains possible in a cross-platform way via PyArray_DescrFromType('p').
The new character codes nN were introduced.
It is now correct to use the Python C-API functions when parsing to npy_intp typed arguments.

(gh-24888)

`numpy.fft.helper` made private

numpy.fft.helper was renamed to numpy.fft._helper to indicate that it is a private submodule. All public functions exported by it should be accessed from numpy.fft.

(gh-24945)

`numpy.linalg.linalg` made private

numpy.linalg.linalg was renamed to numpy.linalg._linalg to indicate that it is a private submodule. All public functions exported by it should be accessed from numpy.linalg.

(gh-24946)

Out-of-bound axis not the same as `axis=None`

In some cases axis=32 or for concatenate any large value was the same as axis=None. Except for concatenate this was deprecate. Any out of bound axis value will now error, make sure to use axis=None.

(gh-25149)

New `copy` keyword meaning for `array` and `asarray` constructors

Now numpy.array and numpy.asarray support three values for copy parameter:

None - A copy will only be made if it is necessary.
True - Always make a copy.
False - Never make a copy. If a copy is required a ValueError is raised.

The meaning of False changed as it now raises an exception if a copy is needed.

(gh-25168)

The `array` special method now takes a `copy` keyword argument.

NumPy will pass copy to the __array__ special method in situations where it would be set to a non-default value (e.g. in a call to np.asarray(some_object, copy=False)). Currently, if an unexpected keyword argument error is raised after this, NumPy will print a warning and re-try without the copy keyword argument. Implementations of objects implementing the __array__ protocol should accept a copy keyword argument with the same meaning as when passed to numpy.array or numpy.asarray.

(gh-25168)

Cleanup of initialization of `numpy.dtype` with strings with commas

The interpretation of strings with commas is changed slightly, in that a trailing comma will now always create a structured dtype. E.g., where previously np.dtype("i") and np.dtype("i,") were treated as identical, now np.dtype("i,") will create a structured dtype, with a single field. This is analogous to np.dtype("i,i") creating a structured dtype with two fields, and makes the behaviour consistent with that expected of tuples.

At the same time, the use of single number surrounded by parenthesis to indicate a sub-array shape, like in np.dtype("(2)i,"), is deprecated. Instead; one should use np.dtype("(2,)i") or np.dtype("2i"). Eventually, using a number in parentheses will raise an exception, like is the case for initializations without a comma, like np.dtype("(2)i").

(gh-25434)

Change in how complex sign is calculated

Following the array API standard, the complex sign is now calculated as z / |z| (instead of the rather less logical case where the sign of the real part was taken, unless the real part was zero, in which case the sign of the imaginary part was returned). Like for real numbers, zero is returned if z==0.

(gh-25441)

Return types of functions that returned a list of arrays

Functions that returned a list of ndarrays have been changed to return a tuple of ndarrays instead. Returning tuples consistently whenever a sequence of arrays is returned makes it easier for JIT compilers like Numba, as well as for static type checkers in some cases, to support these functions. Changed functions are: numpy.atleast_1d, numpy.atleast_2d, numpy.atleast_3d, numpy.broadcast_arrays, numpy.meshgrid, numpy.ogrid, numpy.histogramdd.

`np.unique` `return_inverse` shape for multi-dimensional inputs

When multi-dimensional inputs are passed to np.unique with return_inverse=True, the unique_inverse output is now shaped such that the input can be reconstructed directly using np.take(unique, unique_inverse) when axis=None, and np.take_along_axis(unique, unique_inverse, axis=axis) otherwise.

(gh-25553, gh-25570)

`any` and `all` return booleans for object arrays

The any and all functions and methods now return booleans also for object arrays. Previously, they did a reduction which behaved like the Python or and and operators which evaluates to one of the arguments. You can use np.logical_or.reduce and np.logical_and.reduce to achieve the previous behavior.

(gh-25712)

`np.can_cast` cannot be called on Python int, float, or complex

np.can_cast cannot be called with Python int, float, or complex instances anymore. This is because NEP 50 means that the result of can_cast must not depend on the value passed in. Unfortunately, for Python scalars whether a cast should be considered "same_kind" or "safe" may depend on the context and value so that this is currently not implemented. In some cases, this means you may have to add a specific path for: if type(obj) in (int, float, complex): ....

(gh-26393)

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Edited Dec 09, 2024 by igswsihw-wmadepbot

Update dependency numpy to v2

Release Notes

v2.2.0: 2.2.0 (Dec 8, 2024)

NumPy 2.2.0 Release Notes

Deprecations

Expired deprecations

Compatibility notes

New Features

Improvements

Performance improvements and changes

Changes

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v2.1.3: 2.1.3 (Nov 2, 2024)

NumPy 2.1.3 Release Notes

Improvements

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Pull requests merged

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v2.1.2: 2.1.2 (Oct 5, 2024)

NumPy 2.1.2 Release Notes

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Pull requests merged

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v2.1.1: 2.1.1 (Sep 3, 2024)

NumPy 2.1.1 Release Notes

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Pull requests merged

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v2.1.0: 2.1.0 (Aug 18, 2024)

NumPy 2.1.0 Release Notes

New functions

New function numpy.unstack

Deprecations

Expired deprecations

C API changes

API symbols now hidden but customizable

Many shims removed from npy_3kcompat.h

New PyUFuncObject field process_core_dims_func

New Features

Preliminary Support for Free-Threaded CPython 3.13

f2py can generate freethreading-compatible C extensions

Improvements

histogram auto-binning now returns bin sizes >=1 for integer input data

ndarray shape-type parameter is now covariant and bound to tuple[int, ...]

np.quantile with method closest_observation chooses nearest even order statistic

lapack_lite is now thread safe

The numpy.printoptions context manager is now thread and async-safe

Type hinting numpy.polynomial

Improved numpy.dtypes type hints

Performance improvements and changes

ma.cov and ma.corrcoef are now significantly faster

Changes

ma.corrcoef may return a slightly different result

Cast-safety fixes in copyto and full

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v2.0.2: NumPy 2.0.2 release (Aug 26, 2024)

NumPy 2.0.2 Release Notes

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v2.0.1

NumPy 2.0.1 Release Notes

Improvements

np.quantile with method closest_observation chooses nearest even order statistic

Contributors

Pull requests merged

Checksums

`v2.2.0`: 2.2.0 (Dec 8, 2024)

`v2.1.3`: 2.1.3 (Nov 2, 2024)

`v2.1.2`: 2.1.2 (Oct 5, 2024)

`v2.1.1`: 2.1.1 (Sep 3, 2024)

`v2.1.0`: 2.1.0 (Aug 18, 2024)

New function `numpy.unstack`

New `PyUFuncObject` field `process_core_dims_func`

`f2py` can generate freethreading-compatible C extensions

`histogram` auto-binning now returns bin sizes >=1 for integer input data

`ndarray` shape-type parameter is now covariant and bound to `tuple[int, ...]`

`np.quantile` with method `closest_observation` chooses nearest even order statistic

`lapack_lite` is now thread safe

The `numpy.printoptions` context manager is now thread and async-safe

Type hinting `numpy.polynomial`

Improved `numpy.dtypes` type hints

`ma.cov` and `ma.corrcoef` are now significantly faster

`ma.corrcoef` may return a slightly different result

Cast-safety fixes in `copyto` and `full`

`v2.0.2`: NumPy 2.0.2 release (Aug 26, 2024)

`v2.0.1`

`np.quantile` with method `closest_observation` chooses nearest even order statistic

`v2.0.0`

`__array_prepare__` is removed

`numpy.fft` deprecations for n-D transforms with None values in arguments

`np.linalg.lstsq` now defaults to a new `rcond` value

`loadtxt` and `genfromtxt` default encoding changed

`f2py` compatibility notes

Removed ambiguity when broadcasting in `np.solve`

Modified representation for `Polynomial`

Larger `NPY_MAXDIMS` and `NPY_MAXARGS`, `NPY_RAVEL_AXIS` introduced

`NPY_MAXARGS` not constant and `PyArrayMultiIterObject` size change

Descriptor `elsize` and `alignment` access

`np.add` was extended to work with `unicode` and `bytes` dtypes.

A new `bitwise_count` function

A new Meson backend for `f2py`

`bind(c)` support for `f2py`

A new `strict` option for several testing functions

Add `np.core.umath.find` and `np.core.umath.rfind` UFuncs

`diagonal` and `trace` for `numpy.linalg`

New `long` and `ulong` dtypes

`svdvals` for `numpy.linalg`

A new `isdtype` function

A new `astype` function

New `unique_*` functions

Array API compatible functions for `numpy.linalg`

A `correction` argument for `var` and `std`

`ndarray.device` and `ndarray.to_device`

New keywords for `cholesky` and `pinv`

New keywords for `sort`, `argsort` and `linalg.matrix_rank`

New `numpy.strings` namespace for string ufuncs

`numpy.fft` support for different precisions and in-place calculations

Strings are now supported by `any`, `all`, and the logical ufuncs.

Integer sequences as the shape argument for `memmap`

`errstate` is now faster and context safe

`iso_c_binding` support and improved common blocks for `f2py`

Call `str` automatically on third argument to functions like `assert_equal`

Support for array-like `atol`/`rtol` in `isclose`, `allclose`

n-D FFT transforms allow `s[i] == -1`